Here the long overdue continuation of the "Alien Landscapes" series. This time based on 3DDEMs generated with "Shape from Shading" from single HiRISE images. Enjoy

Click on Images for larger version.

Detail views from PSP_002172_1410 (large gully system)

Detail view of Gullies from PSP_001376_1675

Detail of gully system in PSP_002022_1455

Dune Views from PSP_004339_1890

Detail from PSP_001834_1605

Here is some background info on the making of the images:

"Shape from Shading" (SFS) i.e. the possibility to extract shape information from a single image has always been a fascinating topic for me.Now I found the time to implement a prototype for a new SFS algorithm based on some ideas that I've been thinking about for a long time.The problem with existing SFS approaches (see here for a survey is that they either tend to over-smooth the details (due to the regularization constraint) or suffer from excessive noise in the high-frequency components of the reconstructed surface. Another problem is the large demand on CPU ressources which would make them very challenging to apply to large scale input data, such as HiRISE orbiter images.

So for a long time I was rather sceptical as to the potential of SFS and it was my impression that Methods based on multiple images (stereo) must be far superior to single-image SFS.

However, after a long time of experimenting, combining existing approaches with some new ideas, I got the following quite promising first results that I'd like to share:

All of the images were generated from a single HiRISE image (no depth information was used from stereo or laser altimeter data).Also, no texturing or additional coloring/shading was applied when rendering the surface.Every detail visible is real 3D down to the pixel-level...For rendering I used a very simple model based on lambertian reflection with gouraud shading.

The resolution of the images is still moderate: that is downsampled details crops in the order of 0.5-1 Megapixels. However, despite the heavy math machinery that drives the core of the algoritm (several systems of equations with millions of unknowns) the processing time is still moderate (about 15 Minutes per med-res image, using about 2 Gigs main mem) such that the application to full-res HiRISE images should be possible

The following image shows an example to illustrate the general principle (click to enlarge).

On the left hand side the 2D input image (simple noisy JPEG from the Web with unknwon light source direction). On the right hand side shows the recovered 3D surface re-lighted under a different light source direction.Note that one problem of the current implementation of the algorithm is it's vulnerability to notable distortions in the low frequency components (i.e. large scale variations) of the generated surface. However I'm confident that this can be overcome by an improved version or by adding the large-scale depth information from stereo-based DEMs or altimeter data (MOLA) where available.

I know very little of the field, but isn't this somewhat ground-breaking, revolutionary work that will have significant applications, like, all over the place? Including earth observations? (As a somewhat random example, there's an appeal out for high-res DEMs of Haiti : http://www.boingboing.net/2010/01/15/haiti...#comment-688501 )

Forgive a lay-person's question: would it be possible to drape a texture map or surface image over the DEM, without it looking really ugly and pixellated in places?

QUOTE (Nirgal @ Jan 16 2010, 10:27 PM)

P.S.: I also forgot to mention that in most images the vertical scale is exaggerated by a factor of about 1.5 to 2.

Ah, that's just what I was wondering; those dunes looked a little steep.

I know very little of the field, but isn't this somewhat ground-breaking, revolutionary work that will have significant applications, like, all over the place? Including earth observations?

I would not use such big words like "revolutionary" (this would be waaay too much honor )

The field of single-image "Shape-from-Shading" (SfS) has been a classical area of computer vision since the 1970s (in the planetary science community also known under the name photoclinometry) and it has also been widely applied already for planetary data sets. See for example the seminal work done by Randolph Kirk of USGS in the field.

Among other notable applications, SfS/photoclinometric methods based on MGS/MOC imagery were involved with the MER landing site selections.

The current new method that I'm experimenting with at the moment, does seem to have the potential to improve on existing approaches in particular with respect to the better preserving of high frequency (i.e. detail) variations, whereas on the other hand it too suffers from some of the (fundamental) problems inherent to any single-image method, namely the constant-albedo, shadows, convex-concave-ambiguity, low-frequency distortions to name a few (thats were it could be complemented with the already mentiond combination with stereo- and altimeter-based methods )

However, at this time I'm at a very early stage of experimenting and it's way too early for a final conclusive judgment. I just wanted to share the first results, because I for myself was surprised that they came out rather promising. But there is still a lot of work to do before any definite conclusions can be drawn. Its more of an evolutionary than revolutionary process

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